Multi-level Association Rule Mining for the Discovery of Strong Underrepresented Patterns
The Case Study of Small Dairy Farms in Tanzania
Received: 16 January 2023 | Revised: 27 January 2023 | Accepted: 30 January 2023 | Online: 2 April 2023
Corresponding author: Glory C. Malamsha
Abstract
Increasing the milk production of small dairy producers is necessary to cover the increase in milk demand in Tanzania. Currently, the population of people in both Tanzania and the world has increased and is predicted to increase more in the year 2050. The use of multilevel association rule mining methods to mine strong patterns among smallholder dairy farmers could help in identifying the best dairy farming practices and increase their milk production by adopting them. This study employed multi-level association rule mining to discover strong rules in three clusters, resulting in three levels of rules in each cluster. These three clusters were high, medium, and low milk producers. Rules were obtained for feeding practices, milk production, and breeding and health practices. These rules represent strong patterns among smallholder dairy farmers that could help them improve their dairy farming practices and have a gradual increase in milk production, from low to medium and from medium to higher milk production. Smallholder dairy producers would be provided with recommendations on their dairy farming practices, using rules based on the cluster to which they belong that could help them achieve higher milk production.
Keywords:
association rules mining, dairy farming, smallholder dairy producers, milk yieldDownloads
References
A. S. Osman, "Data Mining Techniques: Review," International Journal of Data Science Research, vol. 2, no. 1, pp. 1–5, Jul. 2019.
T. A. Kumbhare and S. V. Chobe, "An Overview of Association Rule Mining Algorithms," International Journal of Computer Science and Information Technologies, vol. 5, no. 1, pp. 927–930, 2014.
B. Kamsu-Foguem, F. Rigal, and F. Mauget, "Mining association rules for the quality improvement of the production process," Expert Systems with Applications, vol. 40, no. 4, pp. 1034–1045, Mar. 2013. DOI: https://doi.org/10.1016/j.eswa.2012.08.039
T. Karthikeyan and N. Ravikumar, "A Survey on Association Rule Mining," International Journal of Advanced Research in Computer and Communication Engineering, vol. 3, no. 1, pp. 5223–5227, 2014.
A. Ksiksi and H. Amiri, "Using Association Rules to Enrich Arabic Ontology," Engineering, Technology & Applied Science Research, vol. 8, no. 3, pp. 2914–2918, Jun. 2018. DOI: https://doi.org/10.48084/etasr.1998
A. Alqahtani, H. Alhakami, T. Alsubait, and A. Baz, "A Survey of Text Matching Techniques," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6656–6661, Feb. 2021. DOI: https://doi.org/10.48084/etasr.3968
M. Hahsler, "arulesViz: Interactive Visualization of Association Rules with R," The R Journal, vol. 9, no. 2, pp. 163–175, 2017. DOI: https://doi.org/10.32614/RJ-2017-047
D. G. Nyambo, E. T. Luhanga, Z. O. Yonah, F. D. Mujibi, and T. Clemen, "Leveraging peer-to-peer farmer learning to facilitate better strategies in smallholder dairy husbandry," Adaptive Behavior, vol. 30, no. 1, pp. 51–62, Feb. 2022. DOI: https://doi.org/10.1177/1059712320971369
P. Manda, S. Ozkan, H. Wang, F. McCarthy, and S. M. Bridges, "Cross-Ontology Multi-level Association Rule Mining in the Gene Ontology," PLOS ONE, vol. 7, no. 10, 2012, Art. no. e47411. DOI: https://doi.org/10.1371/journal.pone.0047411
J. Tan, "Different Types of Association Rules Mining Review," Applied Mechanics and Materials, vol. 241–244, pp. 1589–1592, 2013. DOI: https://doi.org/10.4028/www.scientific.net/AMM.241-244.1589
P. K. Biswas and S. Liu, "A hybrid recommender system for recommending smartphones to prospective customers," Expert Systems with Applications, vol. 208, Dec. 2022, Art. no. 118058. DOI: https://doi.org/10.1016/j.eswa.2022.118058
V. Rohilla, M. Kaur, and S. Chakraborty, "An Empirical Framework for Recommendation-based Location Services Using Deep Learning," Engineering, Technology & Applied Science Research, vol. 12, no. 5, pp. 9186–9191, Oct. 2022. DOI: https://doi.org/10.48084/etasr.5126
J. Konaté, A. G. Diarra, S. O. Diarra, and A. Diallo, "SyrAgri: A Recommender System for Agriculture in Mali," Information, vol. 11, no. 12, Dec. 2020, Art. no. 561. DOI: https://doi.org/10.3390/info11120561
S. Jaiswal, T. Kharade, N. Kotambe, and S. Shinde, "Collaborative Recommendation System For Agriculture Sector," ITM Web of Conferences, vol. 32, 2020, Art. no. 03034. DOI: https://doi.org/10.1051/itmconf/20203203034
M. Dong, F. Yuan, L. Yao, X. Wang, X. Xu, and L. Zhu, "A survey for trust-aware recommender systems: A deep learning perspective," Knowledge-Based Systems, vol. 249, Aug. 2022, Art. no. 108954. DOI: https://doi.org/10.1016/j.knosys.2022.108954
"2021 Tanzania in Figures," National Bureau of Statistics, Dodoma, Tanzania, Jun. 2022.
"Census Information Dissemination Platform." https://sensa.nbs.go.tz/.
G. Wu et al., "Production and supply of high-quality food protein for human consumption: sustainability, challenges, and innovations," Annals of the New York Academy of Sciences, vol. 1321, no. 1, pp. 1–19, 2014. DOI: https://doi.org/10.1111/nyas.12500
M. J. Boland et al., "The future supply of animal-derived protein for human consumption," Trends in Food Science & Technology, vol. 29, no. 1, pp. 62–73, Jan. 2013. DOI: https://doi.org/10.1016/j.tifs.2012.07.002
G. Rapsomanikis, "The economic lives of smallholder farmers: An analysis based on household data from nine countries," Food and Agriculture Organization of the United Nations, Rome, 2015.
D. Maleko, G. Msalya, A. Mwilawa, L. Pasape, and K. Mtei, "Smallholder dairy cattle feeding technologies and practices in Tanzania: failures, successes, challenges and prospects for sustainability," International Journal of Agricultural Sustainability, vol. 16, no. 2, pp. 201–213, Mar. 2018. DOI: https://doi.org/10.1080/14735903.2018.1440474
D. G. Nyambo, E. T. Luhanga, Z. O. Yonah, and F. D. N. Mujibi, "Application of Multiple Unsupervised Models to Validate Clusters Robustness in Characterizing Smallholder Dairy Farmers," The Scientific World Journal, vol. 2019, Jan. 2019, Art. no. e1020521. DOI: https://doi.org/10.1155/2019/1020521
R. Yadav, "An Improved Multiple-level Association Rule Mining Algorithm with Boolean Transposed Database," International Journal of Computer Science and Information Security, vol. 13, no. 8, pp. 95–104, 2015.
Y. Cheng, W. Yu, and Q. Li, "GA‐based multi-level association rule mining approach for defect analysis in the construction industry," Automation in Construction, vol. 51, pp. 78–91, Mar. 2015. DOI: https://doi.org/10.1016/j.autcon.2014.12.016
F. Mavura, S. M. Pandhare, E. Mkoba, and D. G. Nyambo, "Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters," The Scientific World Journal, vol. 2022, Jun. 2022, Art. no. e6944151. DOI: https://doi.org/10.1155/2022/6944151
D. G. Nyambo, E. T. Luhanga, and Z. O. Yonah, "Characteristics of smallholder dairy farms by association rules mining based on apriori algorithm," International Journal of Society Systems Science, vol. 11, no. 2, pp. 99–118, Jan. 2019. DOI: https://doi.org/10.1504/IJSSS.2019.100101
L. M. Mburu, "Effect of seasonality of feed resources on dairy cattle production in coastal lowlands of Kenya," Ph.D. dissertation, University of Nairobi, Kenya, 2015.
"Hybrid Napier Grass Cultivation For Dairy Animals | Agri Farming," Mar. 07, 2019. https://www.agrifarming.in/hybrid-napier-grass-cultivation-for-dairy-animals.
F. D. N. Mujibi et al., "Performance Evaluation of Highly Admixed Tanzanian Smallholder Dairy Cattle Using SNP Derived Kinship Matrix," Frontiers in Genetics, vol. 10, 2019. DOI: https://doi.org/10.3389/fgene.2019.00375
S. Patil, S. Karunamay, and S. Nath, "Importance of Artificial Insemination in Dairy Farming –," Pashudhan praharee, May 31, 2020. https://www.pashudhanpraharee.com/importance-of-artificial-insemination-in-dairy-farming-4/.
A. R. Chawala, G. Banos, A. Peters, and M. G. G. Chagunda, "Farmer-preferred traits in smallholder dairy farming systems in Tanzania," Tropical Animal Health and Production, vol. 51, no. 6, pp. 1337–1344, Jul. 2019. DOI: https://doi.org/10.1007/s11250-018-01796-9
A. Omore, M. Kidoido, E. Twine, L. Kurwijila, M. O’Flynn, and J. Githinji, "Using ‘theory of change’ to improve agricultural research: recent experience from Tanzania," Development in Practice, vol. 29, no. 7, pp. 898–911, Oct. 2019. DOI: https://doi.org/10.1080/09614524.2019.1641182
K. S. Chaussa, "Assessment of factors affecting performance of dairy cattle kept in smallholder farms in peri-urban areas of Temeke municipality," Ph.D. dissertation, Sokoine University of Agriculture, Morogoro, Tanzania, 2022.
E. Ulicky, J. Magoma, H. Usiri, and A. Edward, "Improving Smallholder Livelihoods: Dairy Production in Tanzania," International Grassland Congress Proceedings, Apr. 2020.
A. H. Blasi, M. A. Abbadi, and R. Al-Huweimel, "Machine Learning Approach for an Automatic Irrigation System in Southern Jordan Valley," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6609–6613, Feb. 2021. DOI: https://doi.org/10.48084/etasr.3944
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